/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.spark.examples.ml;

// $example on$

import org.apache.spark.ml.classification.NaiveBayes;
import org.apache.spark.ml.classification.NaiveBayesModel;
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
// $example off$

/**
 * An example for Naive Bayes Classification.
 */
public class JavaNaiveBayesExample {

    public static void main(String[] args) {
        SparkSession spark = SparkSession
                .builder()
                .appName("JavaNaiveBayesExample")
                .getOrCreate();

        // $example on$
        // Load training data
        Dataset<Row> dataFrame =
                spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt");
        // Split the data into train and test
        Dataset<Row>[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L);
        Dataset<Row> train = splits[0];
        Dataset<Row> test = splits[1];

        // create the trainer and set its parameters
        NaiveBayes nb = new NaiveBayes();

        // train the model
        NaiveBayesModel model = nb.fit(train);

        // Select example rows to display.
        Dataset<Row> predictions = model.transform(test);
        predictions.show();

        // compute accuracy on the test set
        MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
                .setLabelCol("label")
                .setPredictionCol("prediction")
                .setMetricName("accuracy");
        double accuracy = evaluator.evaluate(predictions);
        System.out.println("Test set accuracy = " + accuracy);
        // $example off$

        spark.stop();
    }
}
